Service identification method and apparatus, and system

Abstract

Embodiments of the present application provide a service identification method and apparatus, and a system. The method comprises: receiving packet information from at least one user equipment, wherein the packet information comprises one or more of packet length, packet interval, packet direction, packet quantity, and traffic; performing feature extraction on the packet information to obtain a target feature vector; and performing processing on the basis of the target feature vector to obtain an identified service of the at least one user equipment. In this example, service identification can be achieved without parsing a packet, relying solely on the described packet information, for example, service identification of typical applications such as Tiktok, Facebook and YouTube can be achieved, thereby solving the problem of service identification by network devices in scenarios where the raw bitstream is unavailable and packets are not parsed, ensuring information security.

Description

Business identification method, device, and system

[0001] This application claims priority to the Chinese patent application with application number 202410383268.1 filed with the State Intellectual Property Office of China on March 30, 2024, and priority to the Chinese patent application with the invention name “Business Identification Method, Device and System”, all contents of which are incorporated by reference into this application. Technical Field

[0002] The present application relates to the field of communications, and in particular to a service identification method, device, and system. Background Art

[0003] With the development of the mobile internet, an increasing number of business applications are being carried on wireless networks such as new radio (NR) and long-term evolution (LTE). The 3GPP 23.203 protocol defines the Quality of Service (QoS) requirements for different services carried on wireless networks. Different services can use QoS Class Identifiers (QCIs) to reflect their differentiated QoS requirements. For example, voice calls are carried using QCI1, while video calls are carried using QCI2. These services are Guaranteed Bit Rate (GBR) services, indicating that they can provide the expected bit rate. Other services, such as video, web browsing, email, and FTP, are carried using QCIs 6, 7, 8, and 9. These are non-GBR services, for which the network does not provide a minimum transmission rate guarantee.

[0004] However, QCI resources are limited. The 3GPP protocol defines an 8-bit QCI field with a maximum of 255 values. However, the number of mobile Internet services far exceeds 255, making it impossible to define a specific QCI for each app. Therefore, as shown in the table above, video, Chat (instant messaging, also known as OTT Voice), FTP, and web browsing can all be carried on QCI6. However, these services have identical QoS requirements. For example, FTP downloads require high speeds, while Chat / OTT Voice services are sensitive to latency. Therefore, it is desirable to be able to identify different services to ensure a differentiated experience.

[0005] Currently, service identification is performed using artificial intelligence (AI) technology. It first simulates human operations on the target application, captures the code stream generated during the operation to obtain a pcap file, and then obtains model training data with service type labels based on the pcap file. This data is used to train the service identification model. Through this training, the model can learn the relationship between the model training data and the service type, and realize service identification on the end side. However, because it relies on message parsing for service identification, it violates the principles of privacy protection and pipeline transparency, and there are information security issues. Therefore, this method cannot be used for service identification on network devices. Summary of the Invention

[0006] The present application discloses a service identification method, device and system, which can realize service identification without parsing messages, thereby ensuring information security.

[0007] In a first aspect, an embodiment of the present application provides a service identification method, applied to a network device, comprising:

[0008] receiving message information from at least one user equipment, the message information including one or more of message length, message interval, message direction, message quantity, and flow rate;

[0009] Performing feature extraction on the message information to obtain a target feature vector;

[0010] Processing is performed based on the target feature vector to obtain identification services of the at least one user equipment.

[0011] In an embodiment of the present application, a target feature vector is obtained by extracting features from one or more of the following message information: message length, message interval, message direction, message number, and traffic flow. This feature vector is then processed based on the target feature vector to obtain an identification service for at least one user device. This example does not parse the message, but relies solely on the aforementioned message information to achieve service identification. For example, it can identify typical applications such as Tiktok, Facebook, and Youtube. This solves the problem of network devices performing service identification in scenarios where the original bitstream is unavailable and the message is not parsed, thereby ensuring information security.

[0012] In a possible implementation, the method further includes:

[0013] The message information is segmented and cut to obtain target data;

[0014] The extracting features of the message information to obtain a target feature vector includes:

[0015] Feature extraction is performed on the target data to obtain a target feature vector.

[0016] In this example, the target feature vector is obtained by extracting features from the message information, and the message content is not parsed, which can ensure information security.

[0017] In a possible implementation, the target data includes one or more of a downlink packet length vector, a downlink packet interval vector, a downlink traffic vector, an uplink packet length vector, an uplink packet interval vector, and an uplink traffic vector.

[0018] In a possible implementation, the processing based on the target feature vector to obtain the identification service of the at least one user equipment includes:

[0019] The identification service of the at least one user equipment is obtained by processing based on the target feature vector and a first model, wherein the first model is obtained based on the labeled message information data and the non-labeled message information data.

[0020] In a possible implementation manner, the first model is obtained from a network manager (network management device).

[0021] In a possible implementation, the method further includes:

[0022] Collecting the labeled message information data and the non-labeled message information data;

[0023] Sending first information to a network manager, where the first information includes the labeled message information data and the non-labeled message information data;

[0024] Second information is received from the network manager, where the second information includes the first model.

[0025] In a second aspect, an embodiment of the present application provides a model training method, applied to a network management device, comprising:

[0026] receiving first information from a network device, wherein the first information includes labeled message information data and non-labeled message information data;

[0027] Processing the label message information data and the non-label message information data to obtain first data;

[0028] Performing feature extraction on the first data to obtain a first feature vector;

[0029] Performing model training based on the first feature vector to obtain a first model;

[0030] Second information is sent to the network device, where the second information includes the first model.

[0031] In the embodiment of the present application, the base station-side tracking data is labeled through terminal-side testing, which solves the problem that the base station-side data labels cannot be obtained in the scenario where the data is not unpacked. Moreover, the labeled samples and unlabeled samples collected by the base station side rely on information such as message length, message interval, message flow, message number, and message sending or receiving direction, and perform multi-dimensional feature extraction, processing, model training and reasoning on this information, thereby improving the training speed and reducing the AI ​​training and reasoning computing power overhead. On the other hand, more unlabeled samples on the base station side are obtained as unknown classes, and are cleaned through model training and reasoning to improve the quality of unlabeled samples, thereby improving the recognition accuracy of the model.

[0032] In a possible implementation, the method further includes:

[0033] performing feature selection processing on the first eigenvector to obtain a processed first eigenvector;

[0034] The performing model training based on the first feature vector to obtain a first model includes:

[0035] Model training is performed based on the processed first eigenvector to obtain a first model.

[0036] In a possible implementation, the processing the labeled message information data and the non-labeled message information data to obtain the first data includes:

[0037] Preprocessing the label message information data and the non-label message information data to obtain data of a preset slice unit;

[0038] The data of the preset slice unit is cleaned to obtain first data.

[0039] In a possible implementation, the first data includes one or more of a downlink packet length vector, a downlink packet interval vector, a downlink traffic vector, an uplink packet length vector, an uplink packet interval vector, and an uplink traffic vector.

[0040] In a third aspect, an embodiment of the present application provides a model training method applied to a network device, including:

[0041] Sending first information to the network management device, where the first information includes labeled message information data and non-labeled message information data;

[0042] Second information is received from the network management device, where the second information includes a first model, and the first model is obtained based on the labeled message information data and the non-labeled message information data.

[0043] In a fourth aspect, an embodiment of the present application provides a service identification device, comprising a communication module, configured to receive message information from at least one user equipment, wherein the message information includes one or more of message length, message interval, message direction, message quantity, and flow rate;

[0044] A processing module, configured to extract features from the message information to obtain a target feature vector;

[0045] The processing module is further configured to perform processing based on the target feature vector to obtain the identification service of the at least one user equipment.

[0046] In a possible implementation, the processing module is further configured to segment and cut the message information to obtain target data;

[0047] The processing module is further configured to perform feature extraction on the target data to obtain a target feature vector.

[0048] In a possible implementation, the target data includes one or more of a downlink packet length vector, a downlink packet interval vector, a downlink traffic vector, an uplink packet length vector, an uplink packet interval vector, and an uplink traffic vector.

[0049] In one possible implementation, the processing module is further used to perform processing based on the target feature vector and a first model to obtain identification services of the at least one user device, wherein the first model is obtained based on label message information data and non-label message information data.

[0050] In a possible implementation, the processing module is further configured to collect the label message information data and the non-label message information data;

[0051] The communication module is configured to send first information to the network management, wherein the first information includes the labeled message information data and the non-labeled message information data;

[0052] The communication module is further configured to receive second information from the network manager, where the second information includes the first model.

[0053] In a fifth aspect, an embodiment of the present application provides a communication device, including a communication module, configured to receive first information from a network device, the first information including labeled message information data and unlabeled message information data;

[0054] A processing module, configured to process the label message information data and the non-label message information data to obtain first data;

[0055] The processing module is further configured to perform feature extraction on the first data to obtain a first feature vector;

[0056] The processing module is further configured to perform model training based on the first feature vector to obtain a first model;

[0057] The communication module is further configured to send second information to the network device, where the second information includes the first model.

[0058] In a possible implementation, the processing module is further configured to perform feature selection processing on the first feature vector to obtain a processed first feature vector;

[0059] The processing module is further used to perform model training based on the processed first feature vector to obtain a first model.

[0060] In a possible implementation, the processing module is further configured to pre-process the label message information data and the non-label message information data to obtain data of a preset slice unit;

[0061] The data of the preset slice unit is cleaned to obtain first data.

[0062] In a possible implementation, the first data includes one or more of a downlink packet length vector, a downlink packet interval vector, a downlink traffic vector, an uplink packet length vector, an uplink packet interval vector, and an uplink traffic vector.

[0063] In a sixth aspect, the present application provides a service identification device comprising a processor and a memory; wherein the memory is used to store program code, and the processor is used to call the program code to execute a method provided in any possible implementation manner of the first aspect.

[0064] In a seventh aspect, the present application provides a communication device comprising a processor and a memory; wherein the memory is used to store program code, and the processor is used to call the program code to execute a method provided in any possible implementation manner of the second aspect.

[0065] In an eighth aspect, the present application provides a service identification system, comprising at least one user device and a network device, wherein the network device is used to execute the method provided in any possible implementation manner of the first aspect.

[0066] In the ninth aspect, the present application provides a communication system, including a network management device and a network device, wherein the network management device is used to execute the method provided in any possible implementation manner of the third aspect, and the network device is used to execute the method provided in any possible implementation manner of the second aspect.

[0067] In a tenth aspect, the present application provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and the computer program is executed by a processor to implement the method provided in any possible implementation of the first aspect or the second aspect.

[0068] In an eleventh aspect, the present application provides a computer program product, which, when executed on a computer, enables the computer to execute the method provided in any possible implementation of the first aspect or the second aspect.

[0069] It is understandable that the apparatus described in the fourth aspect, the apparatus described in the fifth aspect, the apparatus described in the sixth aspect, the apparatus described in the seventh aspect, the system described in the eighth aspect, the system described in the ninth aspect, the computer storage medium described in the tenth aspect, or the computer program product described in the eleventh aspect are all used to execute any of the methods provided in the first aspect or any of the methods provided in the second aspect. Therefore, the beneficial effects that can be achieved can refer to the beneficial effects of the corresponding methods and will not be repeated here. BRIEF DESCRIPTION OF THE DRAWINGS

[0070] The following is an introduction to the drawings used in the embodiments of this application.

[0071] FIG1a is a simplified schematic diagram of a wireless communication system provided by an embodiment of the present application;

[0072] FIG1b is a schematic diagram of an architecture of a wireless communication system provided in an embodiment of the present application;

[0073] FIG1c is a schematic diagram of another architecture of a wireless communication system provided in an embodiment of the present application;

[0074] FIG2a is a schematic diagram of a communication system provided by an embodiment of the present application;

[0075] FIG2b is a schematic diagram of another communication system provided in an embodiment of the present application;

[0076] FIG3a is a schematic diagram of a possible application framework in a communication system provided in an embodiment of the present application;

[0077] FIG3 b is a schematic diagram of another possible application framework in the communication system provided in an embodiment of the present application;

[0078] FIG4 is a schematic diagram of an AI application framework provided in an embodiment of the present application;

[0079] FIG5 is a flow chart of a service identification method provided in an embodiment of the present application;

[0080] FIG6 is a schematic diagram of a service identification method provided in an embodiment of the present application;

[0081] FIG7 is a flow chart of a model training method provided in an embodiment of the present application;

[0082] FIG8 is a schematic diagram of a service identification provided by an embodiment of the present application;

[0083] Figures 9a-11 are schematic diagrams of the protocol stack provided in embodiments of the present application;

[0084] FIG12 is a schematic diagram of message information processing provided in an embodiment of the present application;

[0085] FIG13 is a schematic structural diagram of a service identification device provided in an embodiment of the present application;

[0086] FIG14 is a schematic structural diagram of another service identification device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0087] In order to make the objectives, technical solutions and advantages of the present disclosure more clear, the present disclosure will be further described in detail below with reference to the accompanying drawings.

[0088] The present disclosure relates to at least one (item) as follows, indicating one (item) or more (items). More than one (item) refers to two (items) or more than two (items). "And / or" describes the association relationship of associated objects, indicating that three relationships may exist. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. The character " / " generally indicates that the previous and next associated objects are in an "or" relationship. In addition, it should be understood that although the terms first, second, etc. may be used to describe each object in the present disclosure, these objects should not be limited to these terms. These terms are only used to distinguish each object from each other.

[0089] The terms "including" and "having" and any variations thereof mentioned in the following description of the present disclosure are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device that includes a series of steps or units is not limited to the listed steps or units, but optionally includes other steps or units that are not listed, or optionally includes other steps or units that are inherent to these processes, methods, products or devices. It should be noted that in the present disclosure, words such as "exemplary" or "for example" are used to indicate examples, illustrations or explanations. Any method or design described in the present disclosure as "exemplary" or "for example" should not be interpreted as being more preferred or more advantageous than other methods or designs. Specifically, the use of words such as "exemplary" or "for example" is intended to present related concepts in a concrete way.

[0090] It should be noted that in this application, "send" can be understood as "output" and "receive" can be understood as "input". "Send information to A", where "to A" only indicates the direction of information transmission, A is the destination, and does not limit "sending information to A" to direct transmission on the air interface. "Sending information to A" includes sending information directly to A, and also includes sending information indirectly to A through a transmitter, so "sending information to A" can also be understood as "outputting information to A". Similarly, "receiving information from A" indicates that the source of the information is A, including receiving information directly from A, and also including receiving information indirectly from A through a receiver, so "receiving information from A" can also be understood as "inputting information from A".

[0091] It is understood that, in this application, "indication" can include direct indication, indirect indication, explicit indication, and implicit indication. When describing a certain indication information as indicating A, it can be understood that the indication information carries A, directly indicates A, or indirectly indicates A. In this application, the information indicated by the indication information is referred to as the information to be indicated. In the specific implementation process, there are many ways to indicate the information to be indicated. For example, but not limited to, the information to be indicated can be directly indicated, such as the information to be indicated itself or an index of the information to be indicated, or it can be indirectly indicated by indicating other information, where there is an association between the other information and the information to be indicated. It is also possible to indicate only a part of the information to be indicated, while the rest of the information to be indicated is known or agreed in advance. For example, it is also possible to indicate specific information by using a pre-agreed (e.g., protocol-specified) order of arrangement of various information, thereby reducing the indication overhead to a certain extent. The information to be indicated can be sent as a whole or divided into multiple sub-information and sent separately, and the sending period and / or sending time of these sub-information can be the same or different. The specific sending method is not limited in this application. The sending period and / or sending timing of these sub-information may be predefined, for example, predefined according to a protocol, or may be configured by the transmitting end device by sending configuration information to the receiving end device.

[0092] The technology provided by the present disclosure can be applied to various communication systems, for example, the communication system can be a fifth generation (5G) or new radio (NR) system, a long term evolution (LTE) system, an LTE frequency division duplex (FDD) system, an LTE time division duplex (TDD) system, a wireless local area network (WLAN) system, a satellite communication system, a future communication system such as a sixth generation (6G) mobile communication system, or a fusion system of multiple systems. The technical solution provided by the present application can also be applied to device to device (D2D) communication, vehicle-to-everything (V2X) communication, machine to machine (M2M) communication, machine type communication (MTC), and Internet of Things (IoT) communication systems or other communication systems.

[0093] A device in a communication system can send a signal to another device or receive a signal from another device. The signal may include information, signaling, or data, etc. The device can also be replaced by an entity, a network entity, a network element, a communication device, a communication module, a node, a communication node, etc. The present disclosure uses the device as an example for description. For example, the communication system may include at least one terminal device and at least one access network device. The access network device can send a downlink signal to the terminal device, and / or the terminal device can send an uplink signal to the access network device. In addition, it can be understood that if the communication system includes multiple terminal devices, the multiple terminal devices can also send signals to each other, that is, the signal sending device and the signal receiving device can both be terminal devices.

[0094] The information generation method provided in the embodiment of the present application can be applied to wireless communication systems such as 5G, 6G, and satellite communications. Referring to Figure 1a, Figure 1a is a simplified schematic diagram of the wireless communication system provided in the embodiment of the present application. As shown in Figure 1a, the wireless communication system includes a wireless access network 100. The wireless access network 100 can be a next-generation (e.g., 6G or higher) wireless access network, or a traditional (e.g., 5G, 4G, 3G, or 2G) wireless access network. One or more communication devices (120a-120j, collectively referred to as 120) can be connected to each other or to one or more network devices (110a, 110b, collectively referred to as 110) in the wireless access network 100. Optionally, Figure 1a is only a schematic diagram, and the wireless communication system may also include other devices, such as core network devices, wireless relay devices, and / or wireless backhaul devices, which are not shown in Figure 1a.

[0095] Optionally, in actual applications, the wireless communication system may include multiple network devices (also called access network devices) or multiple communication devices at the same time. A network device may serve one or more communication devices at the same time. A communication device may also access one or more network devices at the same time. The embodiments of the present application do not limit the number of communication devices and network devices included in the wireless communication system.

[0096] The network device may be an entity on the network side for transmitting or receiving signals. The network device may be an access device for a communication device to access the wireless communication system in a wireless manner, such as a base station. Base station can broadly cover various names as follows, or be replaced with the following names, such as: NodeB, evolved NodeB (eNB), next generation NodeB (gNB), access network equipment in open radio access network (O-RAN), relay station, access point, transmission point (TRP), transmitting point (TP), master eNodeB (MeNB), secondary eNodeB (SeNB), multi-standard radio (MSR) node, home base station, network controller, access node, wireless node, access point (AP), transmission node, transceiver node, baseband unit (BBU), remote radio unit (RRU), active antenna unit (AAU), remote radio head (RRH), central unit (CU), distributed unit (DU), radio unit ( The base station may be a macro base station, a micro base station, a relay node, a donor node or the like, or a combination thereof. The network device may also refer to a communication module, a modem or a chip provided in the aforementioned device or apparatus. The network device may also be a mobile switching center and a device to device (Device-to-Device, D2D), vehicle-to-everything (V2X), a device that performs the base station function in machine-to-machine (M2M) communications, a network side device in a 6G network, a device that performs the base station function in a future communication system, etc. The network device may support networks with the same or different access technologies. The embodiments of the present application do not limit the specific technology and specific device form adopted by the network device.

[0097] Network devices can be fixed or mobile. For example, base stations 110a, 110b are stationary and are responsible for wireless transmission and reception in one or more cells from communication device 120. The helicopter or drone 120i shown in Figure 1a can be configured to act as a mobile base station, and one or more cells can move according to the location of the mobile base station 120i. In other examples, the helicopter or drone (120i) can be configured to act as a communication device communicating with base station 110b.

[0098] In the present disclosure, the communication device used to implement the above-mentioned access network functions can be an access network device, a network device that has some of the access network functions, or a device that can support the implementation of the access network functions, such as a chip system, a hardware circuit, a software module, or a hardware circuit and a software module. The device can be installed in the access network device or used in conjunction with the access network device. The method of the present disclosure is described using the example of the communication device used to implement the access network device functions being an access network device.

[0099] A communication device may be an entity on the user side for receiving or transmitting signals, such as a mobile phone. A communication device may be used to connect people, objects, and machines. A communication device may communicate with one or more core networks through a network device. A communication device includes a handheld device with wireless connection capabilities, other processing devices connected to a wireless modem, or an in-vehicle device. A communication device may be a portable, pocket-sized, handheld, computer-built-in, or in-vehicle mobile device. The communication device 120 may be widely used in various scenarios, such as cellular communication, device-to-device D2D, vehicle-to-everything V2X, peer-to-peer (P2P), machine-to-machine (M2M), machine-type communication (MTC), Internet of Things (IoT), virtual reality (VR), augmented reality (AR), industrial control, autonomous driving, telemedicine, smart grid, smart furniture, smart office, smart wearables, smart transportation, smart city, drones, robots, remote sensing, passive sensing, positioning, navigation and tracking, autonomous delivery and mobility, etc. Some examples of the communication device 120 include: user equipment (UE) of the 3GPP standard, fixed devices, mobile devices, handheld devices, wearable devices, cellular phones, smart phones, Session Initialization Protocol (SIP) phones, laptops, personal computers, smart books, vehicles, satellites, Global Positioning System (GPS) devices, target tracking devices, drones, helicopters, aircraft, ships, remote control devices, smart home devices, industrial devices, personal communication service (PCS) phones, wireless local loop (WLL) stations, personal digital assistants (PDAs), wireless network cameras, tablet computers, handheld computers, mobile internet devices (MIDs), wearable devices such as smart watches, virtual reality (VR) devices, augmented reality (AR) devices, wireless terminals in industrial control, terminals in vehicle networking systems, wireless terminals in self-driving cars, wireless terminals in smart grids, wireless terminals in transportation safety, and smart cities. Wireless terminals in smart cities, such as smart gas pumps, terminal equipment on high-speed trains, and wireless terminals in smart homes, such as smart speakers, smart coffee machines, and smart printers.The communication device 120 can be a wireless device in the above various scenarios or a device used to be set in a wireless device, for example, a communication module, modem or chip in the above devices. The communication device can also be called a terminal, terminal device, user equipment UE, mobile station (MS), mobile terminal (MT), etc. The communication device can also be a communication device in a future wireless communication system. The communication device can be used in a dedicated network device or a general device. The embodiments of the present application do not limit the specific technology and specific device form adopted by the communication device.

[0100] Alternatively, a communication device can function as a base station. For example, a UE can act as a dispatching entity, providing sidelink signals between UEs in V2X, D2D, or P2P scenarios. As shown in Figure 1a, a cell phone 120a and a car 120b communicate with each other using sidelink signals. Cell phone 120a and smart home device 120e communicate without relaying the communication signals through base station 110b.

[0101] In the present disclosure, a communication device for realizing the functions of a communication device may be a terminal device, or a terminal device having some of the functions of the above communication devices, or a device capable of supporting the functions of the above communication devices, such as a chip system, which may be installed in the terminal device or used in combination with the terminal device. In the present disclosure, a chip system may be composed of a chip, or may include a chip and other discrete devices. In the technical solution provided in the present disclosure, the communication device is described as a terminal device or UE as an example.

[0102] Optionally, a wireless communication system is typically composed of cells, with base stations providing cell management and communication services to multiple mobile stations (MS) in the cell. The base station includes a baseband unit (BBU) and a remote radio unit (RRU). The BBU and RRU can be placed in different locations, for example: the RRU is remote and placed in an area with high traffic volume, while the BBU is placed in a central computer room. The BBU and RRU can also be placed in the same computer room. The BBU and RRU can also be different components under the same rack. Optionally, a cell can correspond to a carrier or component carrier.

[0103] It can be understood that the present disclosure can be applied between a network device and a communication device, between a network device and a network device, or between a communication device and a communication device, that is, between a primary device and a secondary device. The primary device can be a network device or a communication device. When the primary device is a network device, the secondary device can be another network device or a communication device. When the primary device is a communication device, the secondary device can be another communication device.

[0104] The following describes the solution using the example of a primary device being a network device, such as an access network device, and a secondary device being a communication device, such as a terminal device. The downlink direction corresponds to the primary device sending data to the secondary device, and the uplink direction corresponds to the secondary device sending data to the primary device.

[0105] Protocol layer structure between access network equipment and terminal equipment

[0106] The communication between the access network device and the terminal device follows a certain protocol layer structure. The protocol layer structure may include a control plane protocol layer structure and a user plane protocol layer structure. For example, the control plane protocol layer structure may include the functions of protocol layers such as the radio resource control (RRC) layer, the packet data convergence protocol (PDCP) layer, the radio link control (RLC) layer, the medium access control (MAC) layer, and the physical layer. For example, the user plane protocol layer structure may include the functions of protocol layers such as the PDCP layer, the RLC layer, the MAC layer, and the physical layer. In one possible implementation, a service data adaptation protocol (SDAP) layer may also be included above the PDCP layer.

[0107] Optionally, the protocol layer structure between the access network device and the terminal may also include an artificial intelligence (AI) layer for transmitting data related to AI functions.

[0108] Taking data transmission between access network equipment and terminal devices as an example, data transmission needs to pass through the user plane protocol layers, such as the SDAP layer, PDCP layer, RLC layer, MAC layer, and physical layer. The SDAP layer, PDCP layer, RLC layer, MAC layer, and physical layer can also be collectively referred to as the access layer. Data transmission is divided into sending or receiving based on the direction of transmission, and each of these layers is further divided into a sending part and a receiving part. Taking downlink data transmission as an example, after the PDCP layer obtains data from the upper layer, it transmits the data to the RLC layer and MAC layer. The MAC layer then generates a transport block, which is then wirelessly transmitted through the physical layer. Data is encapsulated accordingly in each layer. For example, data received by a layer from the layer above it is considered a service data unit (SDU) of that layer. After encapsulation by that layer, it becomes a protocol data unit (PDU) and is then passed to the next layer.

[0109] For example, a terminal device may also have an application layer and a non-access layer. The application layer can be used to provide services to applications installed in the terminal device. For example, downlink data received by the terminal device can be sequentially transmitted from the physical layer to the application layer, and then provided by the application layer to the application. For another example, the application layer can obtain data generated by the application and sequentially transmit the data to the physical layer for transmission to other communication devices. The non-access layer can be used to forward user data, such as forwarding uplink data received from the application layer to the SDAP layer, or forwarding downlink data received from the SDAP layer to the application layer.

[0110] Structure of access network equipment

[0111] The access network equipment may include a centralized unit (CU) and a distributed unit (DU). Multiple DUs may be centrally controlled by one CU. As an example, the interface between the CU and the DU may be referred to as an F1 interface. Among them, the control plane (CP) interface may be F1-C, and the user plane (UP) interface may be F1-U. The CU and DU may be divided according to the protocol layers of the wireless network: for example, the functions of the PDCP layer and above protocol layers are set in the CU, and the functions of the protocol layers below the PDCP layer (such as the RLC layer and the MAC layer, etc.) are set in the DU; for another example, the functions of the protocol layers above the PDCP layer are set in the CU, and the functions of the protocol layers below the PDCP layer are set in the DU.

[0112] It is understandable that the above division of the processing functions of CU and DU according to the protocol layer is only an example, and can also be divided in other ways, for example, the CU or DU can be divided into functions with more protocol layers, and for example, the CU or DU can also be divided into partial processing functions with the protocol layer. In one design, some functions of the RLC layer and the functions of the protocol layers above the RLC layer are set in the CU, and the remaining functions of the RLC layer and the functions of the protocol layers below the RLC layer are set in the DU. In another design, the functions of the CU or DU can also be divided according to the service type or other system requirements, for example, by delay, the functions whose processing time needs to meet the delay requirements are set in the DU, and the functions that do not need to meet the delay requirements are set in the CU. In another design, the CU can also have one or more functions of the core network. For example, the CU can be set on the network side to facilitate centralized management. In another design, the RU of the DU is set remotely. Among them, the RU has a radio frequency function.

[0113] Optionally, the DU and the RU may be divided at the physical layer (PHY). For example, the DU may implement high-level functions in the PHY layer, and the RU may implement low-level functions in the PHY layer. When used for transmission, the functions of the PHY layer may include adding cyclic redundancy check (CRC) codes, channel coding, rate matching, scrambling, modulation, layer mapping, precoding, resource mapping, physical antenna mapping, and / or RF transmission functions. When used for reception, the functions of the PHY layer may include CRC, channel decoding, rate matching, descrambling, demodulation, layer mapping, channel detection, resource demapping, physical antenna demapping, and / or RF reception functions. The high-level functions in the PHY layer may include a portion of the functions of the PHY layer, such as a portion of the functions that is closer to the MAC layer, and the low-level functions in the PHY layer may include another portion of the functions of the PHY layer, such as a portion of the functions that is closer to the RF functions. For example, the high-level functions in the PHY layer may include adding CRC codes, channel coding, rate matching, scrambling, modulation, and layer mapping, and the low-level functions in the PHY layer may include precoding, resource mapping, physical antenna mapping, and RF transmission functions; or, the high-level functions in the PHY layer may include adding CRC codes, channel coding, rate matching, scrambling, modulation, layer mapping, and precoding, and the low-level functions in the PHY layer may include resource mapping, physical antenna mapping, and RF transmission functions.

[0114] For example, the functions of the CU can be implemented by one entity, or by different entities. For example, the functions of the CU can be further divided, that is, the control plane and the user plane are separated and implemented by different entities, namely the control plane CU entity (i.e., CU-CP entity) and the user plane CU entity (i.e., CU-UP entity). The CU-CP entity and the CU-UP entity can be coupled with the DU to jointly complete the functions of the access network device.

[0115] In the above architecture, signaling generated by the CU can be sent to the terminal device via the DU, and vice versa. For example, RRC or PDCP layer signaling is ultimately processed into physical layer signaling and sent to the terminal device, or converted from received physical layer signaling. In this architecture, the RRC or PDCP layer signaling can be considered to be sent via the DU, or via the DU and RU.

[0116] Optionally, any of the above-mentioned DU, CU, CU-CP, CU-UP, and RU can be a software module, a hardware structure, or a software module + hardware structure, without limitation. The existence forms of different entities can be different and are not limited. For example, DU, CU, CU-CP, and CU-UP are software modules, and RU is a hardware structure. These modules and their execution methods are also within the scope of protection of this disclosure.

[0117] The access network equipment may support one or more types of fronthaul interfaces, and different fronthaul interfaces correspond to DUs and RUs with different functions. If the fronthaul interface between the DU and the RU is a common public radio interface (CPRI), the DU is configured to implement one or more baseband functions, and the RU is configured to implement one or more radio frequency functions. If the fronthaul interface between the DU and the RU is another type of interface, relative to the CPRI, some of the downlink and / or uplink baseband functions, such as precoding, digital beamforming (BF), or one or more of inverse fast Fourier transform (IFFT) / cyclic prefix (CP) for downlink, are moved from the DU to the RU for implementation; and for uplink, one or more of digital beamforming (BF), or fast Fourier transform (FFT) / cyclic prefix (CP) removal are moved from the DU to the RU for implementation. In one possible implementation, the interface may be an enhanced common public radio interface (eCPRI). In the eCPRI architecture, the division between the DU and RU is different, corresponding to different types (category, Cat) of eCPRI, such as eCPRI Cat A, B, C, D, E, and F.

[0118] Taking eCPRI Cat A as an example, for downlink transmission, based on layer mapping, the DU is configured to implement layer mapping and one or more functions preceding it (i.e., one or more of coding, rate matching, scrambling, modulation, and layer mapping). Other functions after layer mapping (e.g., resource element (RE) mapping, digital beamforming (BF), or one or more of inverse fast Fourier transform (IFFT) / cyclic prefix (CP) addition) are moved to the RU for implementation. For uplink transmission, based on RE demapping, the DU is configured to implement demapping and one or more functions preceding it (i.e., one or more of decoding, rate matching, descrambling, demodulation, inverse discrete Fourier transform (IDFT), channel equalization, and RE demapping). Other functions after demapping (e.g., one or more of digital BF or fast Fourier transform (FFT) / CP removal) are moved to the RU for implementation. It is understandable that for the functional description of DU and RU corresponding to various types of eCPRI, reference can be made to the eCPRI protocol, which will not be described in detail here.

[0119] In one possible design, the processing unit for implementing baseband functions in the BBU is called a baseband high layer (BBH) unit, and the processing unit for implementing baseband functions in the RRU / AAU / RRH is called a baseband low layer (BBL) unit.

[0120] In different systems, CU (or CU-CP and CU-UP), DU or RU may also have different names, but those skilled in the art can understand their meanings. For example, in an open radio access network (open RAN, ORAN) system, CU may also be referred to as O-CU (open CU), DU may also be referred to as O-DU, CU-CP may also be referred to as O-CU-CP, CU-UP may also be referred to as O-CU-UP, and RU may also be referred to as O-RU. Any of the CU (or CU-CP, CU-UP), DU and RU in this application may be implemented by a software module, a hardware module, or a combination of a software module and a hardware module.

[0121] In the embodiments of the present application, the device for implementing the functions of the network device can be a network device; it can also be a device that can support the network device to implement the functions, such as a chip system, a hardware circuit, a software module, or a hardware circuit and a software module. The device can be installed in the network device or used in conjunction with the network device. In the embodiments of the present application, only the device for implementing the functions of the network device is used as an example to illustrate, and does not constitute a limitation on the solutions of the embodiments of the present application.

[0122] The network device and / or terminal device can be deployed on land, including indoors or outdoors, handheld or vehicle-mounted; it can also be deployed on the water surface; it can also be deployed on aircraft, balloons and satellites in the air. The embodiments of this application do not limit the scenarios in which the network device and the terminal device are located. In addition, the terminal device and the network device can be hardware devices, or they can be software functions running on dedicated hardware, software functions running on general-purpose hardware, such as virtualization functions instantiated on a platform (e.g., a cloud platform), or entities including dedicated or general-purpose hardware devices and software functions. This application does not limit the specific forms of the terminal device and the network device.

[0123] It should be understood that the number and type of devices in the communication system shown in FIG1a are for illustration only and the present disclosure is not limited thereto. In actual applications, the communication system may include more terminal devices, more access network devices, and other network elements, such as core network devices and / or network elements for implementing artificial intelligence functions. The network element for implementing artificial intelligence functions may be a RAN intelligent controller (RIC).

[0124] As shown in Figure 1b, a 5G base station is connected to the 5G core network (5GC) through the NG port. The 5G base station can be a traditional base station CU and DU as a whole; or it can be a structure including gNodeB-CU and gNodeB-DU, where one gNodeB-CU can connect to multiple gNodeB-DUs, and one gNodeB-DU can only connect to one gNodeB-DU.

[0125] As shown in Figure 1c, an open RAN (O-RAN) architecture is provided in an embodiment of the present application. This architecture includes at least a non-real-time RAN intelligent controller (NRT-RAN Intelligent Controller), a function within the Service Management and Orchestration framework (SMO) that drives content transmitted over the A1 interface. It is composed of a non-RT-RIC framework and non-RT-RIC applications (rApps).

[0126] Near-Real-Time RAN Intelligent Controller: An O-RAN network function (NF) that enables near-real-time control and optimization of RAN elements and resources through fine-grained data collection and manipulation over the E2 interface. It may include AI / ML (artificial intelligence / machine learning) workflows, including model training, inference, and updates.

[0127] Open Cloud (O-Cloud): O-Cloud is a cloud computing platform that includes a set of physical infrastructure nodes that meet O-RAN requirements to host relevant O-RAN functions (such as Near-RT RIC, O-CU-CP, O-CU-UP and O-DU), supporting software components (such as operating system, virtual machine monitoring, container runtime, etc.), and appropriate management and orchestration capabilities.

[0128] Figure 2a is a schematic diagram of a communication system applicable to an embodiment of the present application. As shown in Figure 2a, the communication system 200 may include at least one network device, such as the network device 210 shown in Figure 2a; the communication system 200 may also include at least one terminal device, such as the terminal device 220 and the terminal device 230 shown in Figure 2a. The network device 210 and the terminal device (such as the terminal device 220 and the terminal device 230) can communicate via a wireless link. The communication devices in the communication system, such as the network device 210 and the terminal device 220, can communicate using multi-antenna technology.

[0129] Figure 2b is a schematic diagram of another communication system applicable to an embodiment of the present application. Compared to the communication system 200 shown in Figure 2a, the communication system 300 shown in Figure 2b also includes an AI network element 240. AI network element 240 is used to perform AI-related operations, such as constructing a training dataset or training an AI model.

[0130] In one possible implementation, the network device 210 may send data related to the training of the AI ​​model to the AI ​​network element 240, which constructs a training data set and trains the AI ​​model. For example, the data related to the training of the AI ​​model may include data reported by the terminal device. The AI ​​network element 240 may send the results of the operations related to the AI ​​model to the network device 210, and forward them to the terminal device through the network device 210. For example, the results of the operations related to the AI ​​model may include at least one of the following: an AI model that has completed training, an evaluation result or a test result of the model, etc. Exemplarily, a portion of the trained AI model may be deployed on the network device 210, and another portion may be deployed on the terminal device. Alternatively, the trained AI model may be deployed on the network device 210. Alternatively, the trained AI model may be deployed on the terminal device.

[0131] It should be understood that Figure 2b illustrates only the example of a direct connection between AI network element 240 and network device 210. In other scenarios, AI network element 240 may also be connected to a terminal device. Alternatively, AI network element 240 may be connected to both network device 210 and a terminal device simultaneously. Alternatively, AI network element 240 may be connected to network device 210 via a third-party network element (also referred to as a third-party device or third-party entity). This embodiment of the present application does not limit the connection relationship between the AI ​​network element and other network elements.

[0132] The AI ​​network element 240 may also be provided as a module in a network device and / or a terminal device, for example, in the network device 210 or the terminal device shown in FIG. 2 a .

[0133] It should be noted that Figures 2a and 2b are simplified schematic diagrams for ease of understanding. For example, the communication system may also include other devices, such as wireless relay devices and / or wireless backhaul devices, which are not shown in Figures 2a and 2b. In actual applications, the communication system may include multiple network devices and multiple terminal devices. The embodiments of the present application do not limit the number of network devices and terminal devices included in the communication system.

[0134] In order to support AI technology in wireless networks, AI nodes may also be introduced into the network.

[0135] Optionally, the AI ​​node can be deployed in one or more of the following locations in the communication system: access network equipment, terminal equipment, or core network equipment. Alternatively, the AI ​​node can be deployed separately, for example, in a location other than any of the above devices, such as a host or cloud server in an over-the-top (OTT) system. The AI ​​node can communicate with other devices in the communication system, such as one or more of the following: network equipment, terminal equipment, or core network elements.

[0136] It is understood that this application does not limit the number of AI nodes. For example, when there are multiple AI nodes, the multiple AI nodes can be divided based on function, such as different AI nodes are responsible for different functions.

[0137] It can also be understood that AI nodes can be independent devices, or they can be integrated into the same device to implement different functions, or they can be network elements in hardware devices, or they can be software functions running on dedicated hardware, or they can be virtualized functions instantiated on a platform (for example, a cloud platform). This application does not limit the specific form of the above-mentioned AI nodes.

[0138] An AI node can be an AI network element or an AI module.

[0139] Figure 3a is a schematic diagram of a possible application framework in a communication system. As shown in Figure 3a, network elements in the communication system are connected through interfaces (such as NG, Xn) or air interfaces. One or more AI modules are provided in one or more devices of these network element nodes, such as core network equipment, access network (radio access network, RAN) nodes, terminals or OAM (for the sake of clarity, only one is shown in Figure 3a). The access network node can be a separate RAN node, or it can include multiple RAN nodes, for example, including CU and DU. The CU and / or DU can also be provided with one or more AI modules. Optionally, the CU can also be split into CU-CP and CU-UP. One or more AI models are provided in the CU-CP and / or CU-UP.

[0140] The AI ​​module is used to implement the corresponding AI function. The AI ​​modules deployed in different network elements may be the same or different. The model of the AI ​​module can implement different functions according to different parameter configurations. The model of the AI ​​module can be configured based on one or more of the following parameters: structural parameters (such as the number of neural network layers, the width of the neural network, the connection relationship between layers, the weight of the neuron, the activation function of the neuron, or at least one of the bias in the activation function), input parameters (such as the type of input parameters and / or the dimension of the input parameters), or output parameters (such as the type of output parameters and / or the dimension of the output parameters). Among them, the bias in the activation function can also be called the bias of the neural network.

[0141] An AI module can have one or more models. A model can infer an output, which includes one or more parameters. The learning, training, or inference processes of different models can be deployed on different nodes or devices, or on the same node or device.

[0142] Figure 3b is a schematic diagram of another possible application framework in a communication system. As shown in Figure 3b, the communication system includes a RAN intelligent controller (RIC). For example, the RIC can be the AI ​​module in Figure 3a, which is used to implement AI-related functions. The RIC includes a near-real-time RIC (near-real time RIC, near-RT RIC) and a non-real-time RIC (non-real time RIC, Non-RT RIC). Among them, the non-real-time RIC mainly processes non-real-time information, such as data that is not sensitive to delay, and the delay of this data can be in the order of seconds. The real-time RIC mainly processes near-real-time information, such as data that is relatively sensitive to delay, and the delay of this data is in the order of tens of milliseconds.

[0143] The near real-time RIC is used for model training and reasoning. For example, it is used to train an AI model and use the AI ​​model for reasoning. The near real-time RIC can obtain network-side and / or terminal-side information from a RAN node (e.g., CU, CU-CP, CU-UP, DU, and / or RU) and / or a terminal. This information can be used as training data or reasoning data. Optionally, the near real-time RIC can deliver the reasoning result to the RAN node and / or the terminal. Optionally, the reasoning result can be exchanged between the CU and the DU, and / or between the DU and the RU. For example, the near real-time RIC delivers the reasoning result to the DU, and the DU sends it to the RU.

[0144] The non-real-time RIC is also used for model training and reasoning. For example, it is used to train an AI model and use the model for reasoning. The non-real-time RIC can obtain network-side and / or terminal-side information from RAN nodes (such as CU, CU-CP, CU-UP, DU and / or RU) and / or terminals. This information can be used as training data or reasoning data, and the reasoning results can be submitted to the RAN node and / or terminal. Optionally, the reasoning results can be exchanged between the CU and the DU, and / or between the DU and the RU. For example, the non-real-time RIC submits the reasoning results to the DU, and the DU sends it to the RU.

[0145] The near-real-time RIC and non-real-time RIC may also be separately configured as network elements. Optionally, the near-real-time RIC and non-real-time RIC may also be part of other devices. For example, the near-real-time RIC may be configured in a RAN node (e.g., a CU or DU), while the non-real-time RIC may be configured in an OAM, a server (e.g., a cloud server), a core network device, or other network devices.

[0146] It is understandable that all or part of the functions implemented by one or more of the terminal equipment, access network equipment, core network equipment, or network elements for implementing artificial intelligence functions can be virtualized, that is, implemented by one or more of the proprietary processors or general-purpose processors and the corresponding software modules. Among them, since the terminal equipment and the access network equipment involve interfaces for air interface transmission, the transceiver functions of the interfaces can be implemented by hardware. Core network equipment, such as operation administration and maintenance (OAM) network elements, can be virtualized. Optionally, one or more functions of the virtualized terminal equipment, access network equipment, core network equipment, or network elements for implementing artificial intelligence functions can be implemented by cloud devices, such as cloud devices in over the top (OTT) systems.

[0147] The method provided in the present disclosure can be used for communication between access network equipment and terminal equipment, and can also be used for communication between other communication equipment, such as communication between macro base stations and micro base stations in a wireless backhaul link, and communication between two terminal devices in a side link (SL), etc., without limitation.

[0148] To facilitate understanding of the solutions of the embodiments of the present application, the terms that may be involved in the embodiments of the present application are explained below.

[0149] (1) AI model:

[0150] An AI model is an algorithm or computer program that implements AI functionality. It represents the mapping between the model's inputs and outputs. AI models can be neural networks, linear regression models, decision tree models, support vector machines (SVMs), Bayesian networks, Q-learning models, or other machine learning (ML) models.

[0151] (2) Neural network (NN):

[0152] Neural networks are a specific implementation of AI or machine learning. According to the universal approximation theorem, neural networks can theoretically approximate any continuous function, giving them the ability to learn arbitrary mappings.

[0153] A neural network can be composed of neural units, which can be a computational unit that takes xs and an intercept 1 as input. A neural network is formed by connecting many of these single neural units, meaning that the output of one neural unit can be the input of another. The input of each neural unit can be connected to the local receptive field of the previous layer to extract features from that local receptive field, which can be an area consisting of several neural units.

[0154] Taking the AI ​​model type as a neural network as an example, the AI ​​model involved in this disclosure can be a deep neural network (DNN). Depending on the network construction method, DNN can include feedforward neural networks (FNN), convolutional neural networks (CNN), and recurrent neural networks (RNN).

[0155] (3) Training data set and inference data:

[0156] In the field of machine learning, ground truth usually refers to data that is believed to be accurate or real.

[0157] A training dataset is used to train an AI model. It may include the input to the AI ​​model, or the input and target output of the AI ​​model. A training dataset includes one or more training data. Training data may include training samples input to the AI ​​model, or the target output of the AI ​​model. The target output may also be referred to as a label, sample label, or labeled sample. A label is the true value.

[0158] In the communications field, training datasets can include simulated data collected through simulation platforms, experimental data collected in experimental scenarios, or measured data collected in actual communication networks. Because the geographical environments and channel conditions in which data are generated vary, such as indoor and outdoor locations, mobile speeds, frequency bands, or antenna configurations, the collected data can be categorized during acquisition. For example, data with the same channel propagation environment and antenna configuration can be grouped together.

[0159] Model training essentially involves learning certain characteristics from training data. When training an AI model (such as a neural network), the goal is to ensure that the model's output is as close as possible to the desired predicted value. This is done by comparing the network's predictions with the desired target values. The weight vectors of each layer of the AI ​​model are then updated based on the difference between the two. (Of course, before the first update, there's usually an initialization process, which pre-configures the parameters for each layer of the AI ​​model.) For example, if the network's prediction is too high, the weight vectors are adjusted to predict a lower value. This adjustment is repeated until the AI ​​model predicts the desired target value, or a value very close to it. Therefore, it's necessary to predefine how to compare the difference between the predicted and target values. This is known as the loss function, or objective function. These are important equations used to measure the difference between the predicted and target values. For example, a higher loss function indicates a greater difference. Therefore, training an AI model becomes a process of minimizing this loss, keeping the loss function below a threshold or ensuring that the loss function meets the target requirement. For example, the AI ​​model is a neural network, and adjusting the model parameters of the neural network includes adjusting at least one of the following parameters: the number of layers, width, weights of neurons, or parameters in the activation function of neurons of the neural network.

[0160] Inference data can be used as input to a trained AI model for inference. During the inference process, the inference data is input into the AI ​​model, and the corresponding output is the inference result.

[0161] (4) AI model design:

[0162] The design of an AI model primarily involves data collection (e.g., collecting training data and / or inference data), model training, and model inference. Furthermore, it can also include the application of inference results.

[0163] FIG4 shows an AI application framework.

[0164] In the aforementioned data collection phase, the data source is used to provide training datasets and inference data. In the model training phase, an AI model is obtained by analyzing or training the training data provided by the data source. The AI ​​model represents the mapping relationship between the model's input and output. Learning the AI ​​model through the model training node is equivalent to learning the mapping relationship between the model's input and output using the training data. In the model inference phase, the AI ​​model trained in the model training phase is used to perform inference based on the inference data provided by the data source, obtaining an inference result. This phase can also be understood as inputting the inference data into the AI ​​model and obtaining an output from the AI ​​model, which is the inference result. The inference result can indicate the configuration parameters used (executed) by the execution object and / or the operations performed by the execution object. In the inference result application phase, the inference result is published. For example, the inference result can be centrally planned by the execution (actor) entity, for example, the execution entity can send the inference result to one or more execution objects (e.g., network devices or terminal devices) for execution. Alternatively, the execution entity can provide feedback on the model's performance to the data source to facilitate subsequent model update and training.

[0165] It is understandable that a communication system may include network elements with artificial intelligence capabilities. The above-mentioned AI model design-related steps can be performed by one or more network elements with artificial intelligence capabilities. In one possible design, AI functions (such as AI modules or AI entities) can be configured in existing network elements in the communication system to implement AI-related operations, such as AI model training and / or inference. For example, the existing network element can be a network device or a terminal device. Alternatively, in another possible design, an independent network element can be introduced into the communication system to perform AI-related operations, such as training an AI model. The independent network element can be referred to as an AI network element, an AI node, or an AI entity, etc., and the embodiments of the present application are not limited to these names. For example, the AI ​​network element can be directly connected to the network equipment in the communication system, or it can be indirectly connected to the network equipment through a third-party network element. The third-party network element can be a core network element such as an authentication management function (AMF) network element, a user plane function (UPF) network element, an operation administration and maintenance (OAM) network element, a server (such as a cloud server), an over-the-top (OTT) device, or other network element, without limitation. Exemplarily, the independent AI network element, AI entity, or AI node can be deployed on one or more of the network device side, the terminal device side, or the core network side. Optionally, it can be deployed on a server, such as a cloud server, or an OTT device, or other device. Exemplarily, an AI network element 240 is introduced into the communication system shown in FIG2b. It can be understood that the aforementioned AI module, AI entity, AI network element, or AI node can be used to perform one or more of the AI ​​functions, where the AI ​​functions may include: processing of AI models, such as training and / or updating of AI models, monitoring of AI models, management of AI models, such as registration and / or deregistration of AI models, or application reasoning of AI models.

[0166] The training process of different models can be deployed in different devices or nodes, or in the same device or node. The inference process of different models can be deployed in different devices or nodes, or in the same device or node. Taking the completion of the model training phase of a terminal device as an example, the terminal device can train the matching encoder and decoder, and then send the model parameters of the decoder to the network device. Taking the completion of the model training phase of a network device as an example, after the network device trains the matching encoder and decoder, it can indicate the model parameters of the encoder to the terminal device. Taking the completion of the model training phase of an independent AI network element as an example, the AI ​​network element can train the matching encoder and decoder, and then send the model parameters of the encoder to the terminal device and the model parameters of the decoder to the network device. Then, the model inference phase corresponding to the encoder is performed in the terminal device, and the model inference phase corresponding to the decoder is performed in the network device.

[0167] Among them, the model parameters may include one or more of the following structural parameters of the model (such as the number of layers and / or weights of the model, etc.), the input parameters of the model (such as input dimension, number of input ports), or the output parameters of the model (such as output dimension, number of output ports). It can be understood that the input dimension may refer to the size of an input data. For example, when the input data is a sequence, the input dimension corresponding to the sequence may indicate the length of the sequence. The number of input ports may refer to the number of input data. Similarly, the output dimension may refer to the size of an output data. For example, when the output data is a sequence, the output dimension corresponding to the sequence may indicate the length of the sequence. The number of output ports may refer to the number of output data.

[0168] The method of the embodiment of the present application is described in detail below.

[0169] The above describes the architecture of the embodiment of the present application. The following describes the method of the embodiment of the present application in detail.

[0170] Referring to Figure 5, it is a flow chart of a service identification method provided by an embodiment of the present application. Optionally, the method can be applied to the aforementioned communication system, such as the communication system shown in Figure 1a or Figure 1b. The service identification method shown in Figure 5 may include steps 501-503. It should be understood that this application is described in the order of 501-503 for the convenience of description, and is not intended to limit execution to the above order. The embodiment of the present application does not limit the order of execution, execution time, number of executions, etc. of the above one or more steps. The following description takes the execution subject of steps 501-503 of the service identification method as a network device as an example, and this application is also applicable to other execution subjects. Steps 501-503 are as follows:

[0171] 501. Receive message information from at least one user equipment, where the message information includes one or more of message length, message interval, message direction, message quantity, and traffic.

[0172] The message interval, i.e., the time between two consecutive messages being sent or received, can be calculated using packet timestamps. The message direction can be the message sending direction or the message receiving direction.

[0173] Exemplarily, the network device acquires message information from online users in the existing network.

[0174] 502. Perform feature extraction on the message information to obtain a target feature vector.

[0175] In a possible implementation, the target feature vector includes, for example, one or more of a downlink packet length vector, a downlink packet interval vector, a downlink traffic vector, an uplink packet length vector, an uplink packet interval vector, and an uplink traffic vector.

[0176] In one possible implementation, the network device segments and cuts the message information to obtain target data. Exemplarily, the target data includes one or more of a downlink packet length vector, a downlink packet interval vector, a downlink traffic vector, an uplink packet length vector, an uplink packet interval vector, and an uplink traffic vector.

[0177] Furthermore, the network device extracts features from the target data to obtain a target feature vector.

[0178] For example, the network device divides and cuts the acquired message information into minimum slice units (eg, SliceWindow=1000 packets), and then performs feature calculation on the minimum slice units.

[0179] For another example, the network device performs time-frequency domain feature extraction on the minimum slice unit, first extracting the minimum feature vector (target data), such as the downlink packet length vector, downlink packet interval vector, downlink traffic vector, uplink packet length vector, uplink packet interval vector, uplink traffic vector, etc. Feature extraction is then performed on each feature vector (target data) using a large number (hundreds or thousands) of feature functions, including but not limited to maximum value, minimum value, average value, variance, standard deviation, sum of squares, quantile, autocorrelation coefficient, partial correlation coefficient, and Fourier coefficient. For example, if there are 1,000 feature functions, 6,000 features of these feature vectors (target feature vectors) can be obtained, such as the downlink packet length vector, downlink packet interval vector, downlink traffic vector, uplink packet length vector, uplink packet interval vector, and uplink traffic vector.

[0180] In this example, the target feature vector is obtained by extracting features from the message information, and the message content is not parsed, which can ensure information security.

[0181] 503. Perform processing based on the target feature vector to obtain identification services of the at least one user equipment.

[0182] By processing the target feature vector, the identification service (type) of the at least one user equipment can be obtained, and QoS guarantee is then performed on the identification service, that is, radio resource management algorithm optimization.

[0183] In one possible implementation, processing is performed based on the target feature vector and the first model to obtain an identification service for the at least one user equipment. As shown in Figure 6, the base station selects a user, obtains corresponding message information, preprocesses the message information (service message), and extracts features. The extracted features are then input into the model for inference to ensure QoS for the identified service. If the inference result does not identify the service, the next identification window is entered.

[0184] The first model is obtained based on label message information data and non-label message information data.

[0185] In a possible implementation manner, the first model is obtained from a network management.

[0186] For an introduction to the first model, please refer to the following description and will not be repeated here.

[0187] The above service identification method can also be applied to the system shown in Figure 1c. For example, the GTP-U, SDAP or PDCP module of the base station CU can obtain message information of online users in the existing network (it cannot obtain the complete code stream, but can only obtain message length, message interval (calculated by packet timestamp), and message direction information);

[0188] The base station data processing module divides and cuts the acquired data into minimum slice units (eg, SliceWindow=1000 packets).

[0189] The feature extraction module of the base station CU performs feature calculation on the minimum slice unit.

[0190] The inference module of the base station CU uses the downloaded AI model (classifier) ​​for inference. If the recognition result is the required service, the base station QoS guarantee module is notified through a message.

[0191] The QoS guarantee module of the base station DU provides QoS guarantee for correctly identified services.

[0192] For the introduction of this part, please refer to the description of the embodiment shown in FIG5 , which will not be repeated here.

[0193] In an embodiment of the present application, a target feature vector is obtained by extracting features from one or more of the following message information: message length, message interval, message direction, message number, and traffic flow. This feature vector is then processed based on the target feature vector to obtain an identification service for at least one user device. This example does not parse the message, but relies solely on the aforementioned message information to achieve service identification. For example, it can identify typical applications such as Tiktok, Facebook, and Youtube. This solves the problem of network devices performing service identification in scenarios where the original bitstream is unavailable and the message is not parsed, thereby ensuring information security.

[0194] The first model provided by the embodiment of the present application is introduced below. Referring to Figure 7, it is a flow chart of a model training method provided by the embodiment of the present application. Optionally, the method can be applied to the aforementioned communication system, such as the communication system shown in Figure 1a or Figure 1b or Figure 1c. The service identification method shown in Figure 7 may include steps 701-706. It should be understood that this application is described in the order of 701-706 for the convenience of description, and is not intended to be limited to execution in the above order. The embodiment of the present application does not limit the order of execution, execution time, number of executions, etc. of the above one or more steps. The following description takes the execution subject of steps 701-702 of the service identification method as a network device and the execution subject of steps 703-706 as a network manager as an example. This application is also applicable to other execution subjects. Steps 701-706 are as follows:

[0195] 701. The network device collects labeled message information data and non-labeled message information data.

[0196] The message information data includes one or more of message length, message interval, message direction, message quantity, and flow rate.

[0197] As shown in FIG8 , the model training involves user equipment (terminals), network equipment (base stations) and network management (element management system (EMS) / network management system (NMS)).

[0198] User equipment (terminals) include test terminals and APP test equipment, which support corresponding APP business testing functions and tag recording functions.

[0199] Optionally, users are also involved: they are divided into labeled users and unlabeled users. Labeled users (also known as test engineers) use terminals and app testing equipment in fixed-point or mobile (including driving) scenarios to perform the required app service testing tasks, record the tags, and simultaneously initiate tracking tasks on the network management system. Unlabeled users (also known as background users) are background users who actually use various mobile internet app services.

[0200] Network equipment such as base stations include: a tracking and processing module (GPRS Tunnelling Protocol for the user plane (GTP-U), Service Data Adaptation Protocol (SDAP) or Packet Data Convergence Protocol (PDCP) module, for example, for 5G non-standalone (NSA) mode, the tracking and processing module is GTP-U or PDCP): it reports data after receiving the tracking command issued by the network management. It also includes a data preprocessing module: performs data preprocessing before reasoning. It also includes a feature extraction module: extracts features from the preprocessed data as input to the reasoning module. It also includes a reasoning module: performs business reasoning based on the model issued by the network management, and reports the results to the QoS assurance module for processing. It also includes a QoS assurance module: implements corresponding optimization measures based on the business identification results.

[0201] The EMS is usually the equipment vendor's network management system, which manages the factory's base stations and other equipment in a certain area, and implements network element operations, maintenance, data collection, performance management and other functions. The NMS is usually the operator's network management system, which is responsible for connecting to the EMS of each manufacturer and implementing global resource scheduling, optimization, end-to-end slicing and other functions. The network management includes: tracking and data processing modules: completing data collection, data parsing, label mapping and preprocessing of base station data. It also includes a training module: completing feature extraction, feature selection, training and verification functions based on labeled data, generating a model after successful verification, and further retraining and verification based on labeled data and unlabeled data to form a model function. It also includes an inference module: using model 1 to clean unlabeled data, retaining unlabeled data and deleting data inferred as labeled data.

[0202] For example, tag data (tag message information) collection relies on tag users using test terminals and test equipment to test different app services such as Tiktok, Facebook, and WeChat, performing network drive tests and tag recording (recording the test time of different services). At the same time, the network management sends a command to the base station tracking processing module to initiate base station-side data collection. The base station tracking processing module (GTP-U, SDAP, or PDCP module) initiates message reporting. The reported message cannot obtain the complete code stream, but only information such as message length, message interval, message direction, message number, and flow rate.

[0203] For the collection of untagged data (untagged message information data): Untagged data, for example, is marked as unknown. Randomly select untagged users performing mobile internet services and issue a command to the base station tracking and processing module through the network management system to initiate base station-side data collection. The base station tracking and processing module (GTP-U, SDAP, or PDCP module) initiates message reporting. The reported message cannot obtain the complete code stream, but only information such as message length, message interval (calculated using packet timestamps), message direction, message number, and traffic volume. The randomly selected user may contain tagged data (i.e., data collected through tags such as Tiktok, Facebook, and WeChat) as well as other unknown data, which is labeled as unknown.

[0204] For the RAN architecture shown in Figure 1b, taking 5G SA networking as an example, it does not rely on message parsing, but relies on information such as message length, message interval, message number, message flow, and message sending or receiving direction that can be obtained by the GTP-U, SDAP, or PDCP entity defined in the protocol stack shown in Figure 9a (5G SA NG-U user plane protocol stack) and Figure 9b (5G SA air interface user plane protocol stack). The original data code stream (IP packet) of the APP to be identified is encapsulated and encrypted as shown in Figure 10; different APP services can be identified based on obtaining a sufficient number of labeled samples for the APP to be identified and a sufficient number of unlabeled samples corresponding to the APPs not to be identified. The labeled samples are collected by the network management after people use terminals and test equipment to conduct service tests on the APP to be identified in typical wireless network coverage scenarios. The unlabeled samples are collected by the network management by randomly selecting base stations and cells.

[0205] For another example, the open RAN protocol stack is shown in Figure 11. The protocol stack can be found in Figures 9a to 10 above, and will not be described in detail here.

[0206] Among them, the difference between O-RAN and 3GPP RAN services includes that the base station is divided into O-CU and O-DU modules, the base station inference module is set on the O-CU, and the QoS assurance module is on the O-DU.

[0207] 702. The network device sends first information to the network manager, where the first information includes the labeled message information data and the unlabeled message information data. Correspondingly, the network manager receives the first information from the network device.

[0208] 703. The network manager processes the labeled message information data and the non-labeled message information data to obtain first data.

[0209] In a possible implementation, the label message information data and the non-label message information data are preprocessed to obtain data of a preset slice unit.

[0210] Exemplarily, the data of different app services after the base station side label is preprocessed and cut and segmented with the smallest slicing unit (such as the first n packets within n seconds, such as the first 1000 packets within 2 seconds) as the smallest unit for service identification.

[0211] Then, the data of the preset slice unit is cleaned to obtain first data.

[0212] Exemplary methods include cleaning invalid portions of collected labeled and unlabeled data. For example, if the number of packets within the minimum processing unit time is too small, the segment is discarded. Optionally, it also includes using iterative methods to clean unlabeled data. For example, if a trained model is used for inference and any data identified as labeled data is filtered out from the unlabeled data (unknown labels).

[0213] Based on the above processing, the first data can be obtained.

[0214] In a possible implementation, the first data includes one or more of a downlink packet length vector, a downlink packet interval vector, a downlink traffic vector, an uplink packet length vector, an uplink packet interval vector, and an uplink traffic vector.

[0215] 704. The network manager performs feature extraction on the first data to obtain a first feature vector.

[0216] Exemplarily, as shown in Figure 12, time-frequency domain feature extraction is performed on the minimum slice unit, and the minimum feature vectors are first extracted, such as the downlink packet length vector, the downlink packet interval vector, the downlink traffic vector, the uplink packet length vector, the uplink packet interval vector, the uplink traffic vector, etc. For each feature vector, a large number (such as hundreds or thousands) of feature functions including but not limited to maximum value, minimum value, average value, variance, standard deviation, sum of squares, quantile, autocorrelation coefficient, partial correlation coefficient, Fourier coefficient, etc. are used for feature extraction. For example, if there are 1000 feature functions, 6000 features of these feature vectors such as the downlink packet length vector, the downlink packet interval vector, the downlink traffic vector, the uplink packet length vector, the uplink packet interval vector, and the uplink traffic vector can be obtained.

[0217] 705. The network manager performs model training based on the first feature vector to obtain a first model.

[0218] In a possible implementation, feature selection processing is performed on the first feature vector to obtain a processed first feature vector.

[0219] For example, feature selection is performed based on hypothesis testing, correlation testing, and model training. Without affecting the accuracy of the model, the most important features are selected using methods such as feature hypothesis testing such as P-value test, correlation testing such as Pearson correlation test, Sequential Forward Selection (SFS) algorithm, or Recursive Feature Elimination (RFE) algorithm. For example, 10 to 100 features are selected to reduce computing power overhead.

[0220] Furthermore, model training is performed based on the processed first eigenvector to obtain a first model.

[0221] Exemplarily, based on the selected features, AI algorithms including but not limited to XGBoost, GBDT, LightGBM, Random_Forest, SVM, Ridge_Regression, MLP, KNN, etc. are used for model training to form AI models of different APP business features and AI models for non-required identification of APP business features (unknown). For example, to identify the three businesses of Tiktok, Facebook, and Wechat, an AI model including the business features of Tiktok, Facebook, Wechat, and unknown is generated, or deep learning algorithms such as CNN are used for model training, etc. This solution does not impose any restrictions on the training algorithms, etc.

[0222] 706. The network manager sends second information to the network device, where the second information includes the first model. Correspondingly, the network device receives the second information from the network manager.

[0223] In one possible implementation, the network administrator verifies the first model. For example, 80% of the samples are randomly selected for training and 20% for inference. The model's accuracy, recall, and precision are verified. Once the accuracy, recall, and precision reach a certain threshold, such as 90%, the model is considered stable. If the model's accuracy, recall, and precision do not reach the threshold, retraining is required, including supplementing validation data and optimizing algorithms and parameters. The network administrator then sends the trained model to the network device.

[0224] This example uses the network management as the execution entity of steps 703-706. It should be noted that the model training process can also be completed independently on the network equipment, for example, using a near-real-time RAN intelligent controller (as shown in Figure 1c) for model training, and this solution does not impose any restrictions on this.

[0225] In the embodiment of the present application, the base station-side tracking data is labeled through terminal-side testing, which solves the problem that the base station-side data labels cannot be obtained in the scenario where the data is not unpacked. Moreover, the labeled samples and unlabeled samples collected by the base station side rely on information such as message length, message interval, message flow, message number, and message sending or receiving direction, and perform multi-dimensional feature extraction, processing, model training and reasoning on this information, thereby improving the training speed and reducing the AI ​​training and reasoning computing power overhead. On the other hand, more unlabeled samples on the base station side are obtained as unknown classes, and are cleaned through model training and reasoning to improve the quality of unlabeled samples, thereby improving the recognition accuracy of the model.

[0226] It should be noted that in the various embodiments of the present application, unless otherwise specified or there is a logical conflict, the terms and / or descriptions between the various embodiments are consistent and can be referenced by each other. The technical features in different embodiments can be combined to form new embodiments according to their internal logical relationships.

[0227] The above describes in detail the method of the embodiment of the present application, and the following provides the device of the embodiment of the present application. It will be understood that in the various device embodiments of the present application, the division of multiple units or modules is only a logical division based on function, and is not intended to limit the specific structure of the device. In a specific implementation, some functional modules may be subdivided into more small functional modules, and some functional modules may be combined into one functional module, but no matter whether these functional modules are subdivided or combined, the general process performed by the device is the same. For example, some devices include a receiving unit and a sending unit. In some designs, the sending unit and the receiving unit can also be integrated into a communication unit, which can implement the functions implemented by the receiving unit and the sending unit. Typically, each unit corresponds to its own program code (or program instructions), and when the program code corresponding to each of these units runs on the processor, the unit is controlled by the processing unit to execute the corresponding process to implement the corresponding function.

[0228] The embodiments of the present application also provide an apparatus for implementing any of the above methods. For example, a service identification apparatus is provided, which includes modules (or means) for implementing each step performed by the network device in any of the above methods.

[0229] For example, referring to FIG13 , which is a schematic diagram of the structure of a service identification device provided in an embodiment of the present application, the service identification device is used to implement the aforementioned service identification method, such as the service identification method shown in FIG5 .

[0230] As shown in FIG13 , the apparatus may include a communication module 1301 and a processing module 1302 , specifically as follows:

[0231] The communication module 1301 is configured to receive message information from at least one user equipment, where the message information includes one or more of message length, message interval, message direction, message quantity, and flow rate;

[0232] The processing module 1302 is configured to extract features from the message information to obtain a target feature vector;

[0233] The processing module 1302 is further configured to perform processing based on the target feature vector to obtain the identification service of the at least one user equipment.

[0234] In a possible implementation, the processing module 1302 is further configured to segment and cut the message information to obtain target data;

[0235] The processing module 1302 is further configured to perform feature extraction on the target data to obtain a target feature vector.

[0236] In a possible implementation, the target data includes one or more of a downlink packet length vector, a downlink packet interval vector, a downlink traffic vector, an uplink packet length vector, an uplink packet interval vector, and an uplink traffic vector.

[0237] In one possible implementation, the processing module 1302 is further used to perform processing based on the target feature vector and the first model to obtain the identification service of the at least one user device, wherein the first model is obtained based on the label message information data and the non-label message information data.

[0238] In a possible implementation, the processing module 1302 is further configured to collect the label message information data and the non-label message information data;

[0239] The communication module 1301 is configured to send first information to a network management system, where the first information includes the labeled message information data and the non-labeled message information data;

[0240] The communication module 1301 is further configured to receive second information from the network manager, where the second information includes the first model.

[0241] For another example, an embodiment of the present application further provides a communication device. The communication device is used to implement the aforementioned model training method. The communication device may include a communication module and a processing module, wherein:

[0242] A communication module, configured to receive first information from a network device, wherein the first information includes labeled message information data and non-labeled message information data;

[0243] A processing module, configured to process the label message information data and the non-label message information data to obtain first data;

[0244] The processing module is further configured to perform feature extraction on the first data to obtain a first feature vector;

[0245] The processing module is further configured to perform model training based on the first feature vector to obtain a first model;

[0246] The communication module is further configured to send second information to the network device, where the second information includes the first model.

[0247] In a possible implementation, the processing module is further configured to perform feature selection processing on the first feature vector to obtain a processed first feature vector;

[0248] The processing module is further used to perform model training based on the processed first feature vector to obtain a first model.

[0249] In a possible implementation, the processing module is further configured to pre-process the label message information data and the non-label message information data to obtain data of a preset slice unit;

[0250] The data of the preset slice unit is cleaned to obtain first data.

[0251] In a possible implementation, the first data includes one or more of a downlink packet length vector, a downlink packet interval vector, a downlink traffic vector, an uplink packet length vector, an uplink packet interval vector, and an uplink traffic vector.

[0252] The introduction of the above modules can be found in the description of the aforementioned embodiments and will not be repeated here.

[0253] It should be understood that the division of the modules in the above-mentioned devices is only a division of logical functions. In actual implementation, they can be fully or partially integrated into one physical entity, or they can be physically separated. In addition, the modules in the service identification device can be implemented in the form of a processor calling software; for example, the service identification device includes a processor, the processor is connected to a memory, and the memory stores instructions. The processor calls the instructions stored in the memory to implement any of the above methods or realize the functions of the modules of the device, wherein the processor is, for example, a general-purpose processor, such as a central processing unit (CPU) or a microprocessor, and the memory is a memory within the device or a memory outside the device. Alternatively, the modules in the device can be implemented in the form of hardware circuits, and the functions of some or all units can be realized by designing the hardware circuits. The hardware circuit can be understood as one or more processors. For example, in one implementation, the hardware circuit is an application-specific integrated circuit (ASIC), which realizes the functions of some or all of the above units by designing the logical relationship of the components in the circuit. For another example, in another implementation, the hardware circuit can be implemented by a programmable logic device (PLD). Taking a field programmable gate array (FPGA) as an example, it can include a large number of logic gate circuits, and the connection relationship between the logic gate circuits is configured by configuring the configuration file, thereby realizing the functions of some or all of the above units. All modules of the above devices can be implemented in the form of software called by the processor, or in the form of hardware circuits, or in part by the form of software called by the processor, and the rest by hardware circuits.

[0254] FIG14 is a schematic diagram illustrating the hardware structure of another service identification device provided in an embodiment of the present application. As shown in FIG14 , service identification device 1400 (which may be a computer device) includes a memory 1401, a processor 1402, a communication interface 1403, and a bus 1404. The memory 1401, processor 1402, and communication interface 1403 are interconnected via bus 1404.

[0255] The memory 1401 may be a read-only memory (ROM), a static storage device, a dynamic storage device, or a random access memory (RAM).

[0256] The memory 1401 can store programs. When the program stored in the memory 1401 is executed by the processor 1402, the processor 1402 and the communication interface 1403 are used to perform the various steps of the service identification method of the embodiment of the present application.

[0257] Processor 1402 is a circuit with signal processing capabilities. In one implementation, processor 1402 can be a circuit with instruction reading and execution capabilities, such as a central processing unit (CPU), a microprocessor, a graphics processing unit (GPU) (which can be understood as a microprocessor), or a digital signal processor (DSP). In another implementation, processor 1402 can implement certain functions through the logical relationship of a hardware circuit. The logical relationship of the hardware circuit is fixed or reconfigurable. For example, processor 1402 is a hardware circuit implemented by an ASIC or a programmable logic device (PLD), such as an FPGA. In a reconfigurable hardware circuit, the process of the processor loading a configuration file and implementing the hardware circuit configuration can be understood as the process of the processor loading instructions to implement the functions of some or all of the above modules. In addition, it can also be a hardware circuit designed for artificial intelligence, which can be understood as an ASIC, such as a neural network processing unit (NPU), a tensor processing unit (TPU), a deep learning processing unit (DPU), etc. The processor 1402 is used to execute relevant programs to implement the functions required to be performed by the units in the service identification device of the embodiment of the present application, or to execute the service identification method of the method embodiment of the present application.

[0258] It can be seen that each module in the above device can be one or more processors (or processing circuits) configured to implement the above method, such as: CPU, GPU, NPU, TPU, DPU, microprocessor, DSP, ASIC, FPGA, or a combination of at least two of these processor forms.

[0259] In addition, the modules in the above device can be fully or partially integrated together, or can be implemented independently. In one implementation, these modules are integrated together and implemented in the form of a system-on-a-chip (SOC). The SOC may include at least one processor for implementing any of the above methods or implementing the functions of the modules of the device. The type of the at least one processor can be different, for example, including a CPU and FPGA, a CPU and an artificial intelligence processor, a CPU and a GPU, etc.

[0260] The communication interface 1403 uses a transceiver device such as, but not limited to, a transceiver to implement communication between the apparatus 1400 and other devices or a communication network. For example, data can be obtained through the communication interface 1403.

[0261] The bus 1404 may include a path for transmitting information between various components of the device 1400 (eg, the memory 1401 , the processor 1402 , and the communication interface 1403 ).

[0262] It should be noted that although the device 1400 shown in FIG14 only shows a memory, a processor, and a communication interface, during the specific implementation process, those skilled in the art will understand that the device 1400 also includes other components necessary for normal operation. At the same time, according to specific needs, those skilled in the art will understand that the device 1400 may also include hardware components that implement other additional functions. Furthermore, those skilled in the art will understand that the device 1400 may also include only the components necessary to implement the embodiments of the present application, and does not necessarily include all of the components shown in FIG14.

[0263] An embodiment of the present application also provides a computer-readable storage medium, which stores instructions. When the computer-readable storage medium is executed on a computer or a processor, the computer or processor executes one or more steps in any of the above methods.

[0264] The present application also provides a computer program product comprising instructions, which, when executed on a computer or processor, causes the computer or processor to execute one or more steps in any of the above methods.

[0265] It should be understood that in the description of this application, unless otherwise specified, " / " indicates that the objects associated with each other are in an "or" relationship. For example, A / B can mean A or B; where A and B can be singular or plural. Also, in the description of this application, unless otherwise specified, "multiple" means two or more than two. "At least one of the following" or similar expressions refers to any combination of these items, including any combination of single or plural items. For example, at least one of a, b, or c can mean: a, b, c, ab, ac, bc, or abc, where a, b, and c can be single or plural. In addition, to facilitate the clear description of the technical solutions of the embodiments of this application, in the embodiments of this application, words such as "first" and "second" are used to distinguish between identical or similar items with substantially the same functions and effects. Those skilled in the art will understand that words such as "first" and "second" do not limit the quantity or execution order, and words such as "first" and "second" do not necessarily mean different. At the same time, in the embodiments of this application, words such as "exemplary" or "for example" are used to indicate examples, illustrations, or descriptions. Any embodiment or design described as "exemplary" or "for example" in the embodiments of this application should not be interpreted as being more preferred or more advantageous than other embodiments or designs. Rather, the use of words such as "exemplary" or "for example" is intended to present the relevant concepts in a concrete manner to facilitate understanding.

[0266] In the several embodiments provided in this application, it should be understood that the disclosed systems, devices, and methods can be implemented in other ways. For example, the division of the units is only a logical function division, and there may be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. The mutual coupling, direct coupling, or communication connection shown or discussed can be through some interface, indirect coupling or communication connection of devices or units, and can be electrical, mechanical or other forms.

[0267] Units described as separate components may or may not be physically separate, and components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of these units may be selected to achieve the purpose of this embodiment according to actual needs.

[0268] In the above embodiments, all or part of the embodiments may be implemented by software, hardware, firmware, or any combination thereof. When implemented using software, all or part of the embodiments may be implemented in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, all or part of the processes or functions according to the embodiments of the present application are generated. The computer may be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions may be stored in a computer-readable storage medium or transmitted via the computer-readable storage medium. The computer instructions may be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via a wired (e.g., coaxial cable, optical fiber, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) method. The computer-readable storage medium may be any available medium that a computer can access or a data storage device such as a server or data center that includes one or more available media integrated therein. The available medium may be a read-only memory (ROM), a random access memory (RAM), a magnetic medium such as a floppy disk, a hard disk, a tape, a magnetic disk, or an optical medium such as a digital versatile disc (DVD), or a semiconductor medium such as a solid state disk (SSD).

[0269] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any changes or substitutions within the technical scope disclosed in the present invention should be included in the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be based on the scope of protection of the claims.

Claims

1. A service identification method, applied to a network device, characterized in that: include: receiving message information from at least one user equipment, the message information including one or more of message length, message interval, message direction, message quantity, and flow rate; Performing feature extraction on the message information to obtain a target feature vector; Processing is performed based on the target feature vector to obtain identification services of the at least one user equipment.

2. The method according to claim 1, characterized in that Also includes: The message information is segmented and cut to obtain target data; The extracting features of the message information to obtain a target feature vector includes: Feature extraction is performed on the target data to obtain a target feature vector.

3. The method according to claim 1 or 2, characterized in that The target data includes one or more of a downlink packet length vector, a downlink packet interval vector, a downlink traffic vector, an uplink packet length vector, an uplink packet interval vector, and an uplink traffic vector.

4. The method according to any one of claims 1 to 3, characterized in that The processing based on the target feature vector to obtain the identification service of the at least one user equipment includes: The identification service of the at least one user equipment is obtained by processing based on the target feature vector and a first model, wherein the first model is obtained based on the labeled message information data and the non-labeled message information data.

5. The method according to claim 4, characterized in that Also includes: Collecting the labeled message information data and the non-labeled message information data; Sending first information to a network management device, where the first information includes the labeled message information data and the non-labeled message information data; Second information is received from the network manager, where the second information includes the first model.

6. A model training method, applied to a network management device, characterized in that: include: receiving first information from a network device, wherein the first information includes labeled message information data and non-labeled message information data; Processing the label message information data and the non-label message information data to obtain first data; Performing feature extraction on the first data to obtain a first feature vector; Performing model training based on the first feature vector to obtain a first model; Second information is sent to the network device, where the second information includes the first model.

7. The method according to claim 6, characterized in that Also includes: performing feature selection processing on the first eigenvector to obtain a processed first eigenvector; The performing model training based on the first feature vector to obtain a first model includes: Model training is performed based on the processed first feature vector to obtain a first model.

8. The method according to claim 6 or 7, characterized in that The processing of the label message information data and the non-label message information data to obtain the first data includes: Preprocessing the label message information data and the non-label message information data to obtain data of a preset slice unit; The data of the preset slice unit is cleaned to obtain first data.

9. The method according to any one of claims 6 to 8, characterized in that The first data includes one or more of a downlink packet length vector, a downlink packet interval vector, a downlink traffic vector, an uplink packet length vector, an uplink packet interval vector, and an uplink traffic vector.

10. A model training method, applied to a network device, characterized in that: include: Sending first information to the network management device, where the first information includes labeled message information data and non-labeled message information data; Second information is received from the network management device, where the second information includes a first model, and the first model is obtained based on the labeled message information data and the non-labeled message information data.

11. A service identification device, applicable to equipment and chips, characterized in that: The method comprises a processor and a memory; wherein the memory is used to store program code, and the processor is used to call the program code to execute the method according to any one of claims 1 to 5.

12. A service identification device, characterized in that: The method comprises a module for implementing the method according to any one of claims 1 to 5.

13. A business identification system, characterized in that: The system comprises at least one user equipment and a network device, wherein the network device is configured to execute the method according to any one of claims 1 to 5.

14. A communication device, applicable to devices and chips, characterized in that: The system comprises a processor and a memory; wherein the memory is used to store program code, and the processor is used to call the program code to execute the method according to any one of claims 6 to 10.

15. A communication device, characterized in that: The method comprises a module for implementing the method according to any one of claims 6 to 10.

16. A communication system, characterized in that: The method comprises a network management device and a network device, wherein the network device is used to execute the method according to claim 10, and the network management device is used to execute the method according to any one of claims 6 to 9.

17. A computer-readable storage medium, characterized in that The computer-readable storage medium stores a computer program, and the computer program is executed by a processor to implement the method according to any one of claims 1 to 10.

18. A computer program product, characterized in that When the computer program product is run on a computer, the computer is caused to perform the method according to any one of claims 1 to 10.

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